Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity

Posada-Quintero, Hugo F.; Bolkhovsky, Jeffrey; Qin, Murong; Chon, Ki H.

doi:10.1177/0018720818781196

Cited by 27 publications

(24 citation statements)

References 51 publications

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“…By the end of the 24 h, the performance exhibited a recovery, which suggests the ANS is re-activated. This has also been observed in the recovering performance reported in previous studies and is thought to be related to the circadian rhythm [11,44,45].…”

Section: Discussionsupporting

confidence: 83%

“…KNN and LSVM exhibited a reduction in accuracy on the classification between 20 and 22 h. Previous studies have found a reduction in performance at 22 h of wakefulness [11,44,45]. The low accuracy suggests that the activation of the ANS was diminished (or at least different) after 20 h of wakefulness, compared to the trials in normal conditions (first trials).…”

Section: Discussionmentioning

confidence: 81%

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Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Posada-Quintero

Bolkhovsky

2019

Behavioral Sciences

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Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 81%

Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Posada-Quintero

Bolkhovsky

2019

Behavioral Sciences

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“…To evaluate this, we have employed a simple yet robust test that requires the subject to maintain response inhibition and reactivity. This information from the neurophysiological measures of the oscillatory activity of the brain can complement physiological measures used in studies focused on autonomic reactions (Posada-Quintero et al, 2017, 2018), to predict the effect of sleep deprivation on human autonomic response and performance.…”

Section: Introductionmentioning

confidence: 99%

Brain Activity Correlates With Cognitive Performance Deterioration During Sleep Deprivation

et al. 2019

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We studied the correlation between oscillatory brain activity and performance in healthy subjects performing the error awareness task (EAT) every 2 h, for 24 h. In the EAT, subjects were shown on a screen the names of colors and were asked to press a key if the name of the color and the color it was shown in matched, and the screen was not a duplicate of the one before (“Go” trials). In the event of a duplicate screen (“Repeat No-Go” trial) or a color mismatch (“Stroop No-Go” trial), the subjects were asked to withhold from pressing the key. We assessed subjects’ (N = 10) response inhibition by measuring accuracy of the “Stroop No-Go” (SNGacc) and “Repeat No-Go” trials (RNGacc). We assessed their reactivity by measuring reaction time in the “Go” trials (GRT). Simultaneously, nine electroencephalographic (EEG) channels were recorded (Fp2, F7, F8, O1, Oz, Pz, O2, T7, and T8). The correlation between reactivity and response inhibition measures to brain activity was tested using quantitative measures of brain activity based on the relative power of gamma, beta, alpha, theta, and delta waves. In general, response inhibition and reactivity reached a steady level between 6 and 16 h of sleep deprivation, which was followed by sustained impairment after 18 h. Channels F7 and Fp2 had the highest correlation to the indices of performance. Measures of response inhibition (RNGacc and SNGacc) were correlated to the alpha and theta waves’ power for most of the channels, especially in the F7 channel (r = 0.82 and 0.84, respectively). The reactivity (GRT) exhibited the highest correlation to the power of gamma waves in channel Fp2 (0.76). We conclude that quantitative measures of EEG provide information that can help us to better understand changes in subjects’ performance and could be used as an indicator to prevent the adverse consequences of sleep deprivation.

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“…Our choice of 20 subjects allows greater than 95% confidence interval to observe a significant effect (p < .05 ;Faul, Erdfelder, Lang, & Buchner, 2007;Sundelin et al, 2013). Furthermore, this sample size is sufficient to detect a correlation of at least 0.8 between PVT and facial indices at the 0.05 level of significance (Dorrian, Rogers, & Dinges, 2005;Posada-Quintero et al, 2018;Zar, 1999).…”

Section: Participantsmentioning

confidence: 98%

“…To address these issues, researchers have been working to develop effective methods to detect and/or predict decrements in performance due to prolonged wakefulness before costly mistakes are made. These efforts include vocal features, electrodermal activity signals, and heart rate variability (McGlinchey et al, 2011;Posada-Quintero, Bolkhovsky, Qin, & Chon, 2018;Sloboda et al, 2018). However, vocal features are not easy to document in noise environment, and the other biosignal methods are limited in that they are sensitive to motion artifacts, expensive, and require electrodes to be attached to the skin/body.…”

mentioning

confidence: 99%

Facial features and head movements obtained with a webcam correlate with performance deterioration during prolonged wakefulness

Kong

Posada-Quintero

Daley

et al. 2020

Atten Percept Psychophys

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We have performed a direct comparison between facial features obtained from a webcam and vigilance-task performance during prolonged wakefulness. Prolonged wakefulness deteriorates working performance due to changes in cognition, emotion, and by delayed response. Facial features can be potentially collected everywhere using webcams located in the workplace. If this type of device can obtain relevant information to predict performance deterioration, this technology can potentially reduce serious accidents and fatality. We extracted 34 facial indices, including head movements, facial expressions, and perceived facial emotions from 20 participants undergoing the psychomotor vigilance task (PVT) over 25 hours. We studied the correlation between facial indices and the performance indices derived from PVT, and evaluated the feasibility of facial indices as detectors of diminished reaction time during the PVT. Furthermore, we tested the feasibility of classifying performance as normal or impaired using several machine learning algorithms with correlated facial indices. Twenty-one indices were found significantly correlated with PVT indices. Pitch, from the head movement indices, and four perceived facial emotions-anger, surprise, sadness, and disgust-exhibited significant correlations with indices of performance. The eye-related facial expression indices showed especially strong correlation and higher feasibility of facial indices as classifiers. Significantly correlated indices were shown to explain more variance than the other indices for most of the classifiers. The facial indices obtained from a webcam strongly correlate with working performance during 25 hours of prolonged wakefulness.

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Human Performance Deterioration Due to Prolonged Wakefulness Can Be Accurately Detected Using Time-Varying Spectral Analysis of Electrodermal Activity

Abstract: This can be used to detect the adverse effects of prolonged wakefulness on human performance.

Cited by 27 publications

References 51 publications

Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Brain Activity Correlates With Cognitive Performance Deterioration During Sleep Deprivation

Facial features and head movements obtained with a webcam correlate with performance deterioration during prolonged wakefulness

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